Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction
Authors: Stanley Simoes, Deepak P, Munu Sairamesh, Deepak Khemani, Sameep Mehta
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach. We perform our empirical evaluation on a variety of extraction tasks over multiple real-world document corpora as shown in Table 2. |
| Researcher Affiliation | Collaboration | Stanley Simoes Indian Institute of Technology Madras EMAIL Deepak P Queen s University Belfast EMAIL Munu Sairamesh Indian Institute of Technology Madras EMAIL Deepak Khemani Indian Institute of Technology Madras EMAIL Sameep Mehta IBM Research India EMAIL |
| Pseudocode | Yes | Algorithm 1 MATCH-SET-EXPANSION |
| Open Source Code | Yes | 4Source code available at https://github.com/stanleyts/ Content NContext |
| Open Datasets | Yes | The talk.politics.mideast and misc.forsale corpora are taken from the 20 Newsgroups dataset6, whereas the Enron corpus is a random subset of 100k documents from the Enron Email Dataset7. The Web KB corpus8 is another popular document dataset. 6http://qwone.com/ jason/20Newsgroups/ 7https://www.cs.cmu.edu/ ./enron/ 8http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo51/www/co-training/data/ |
| Dataset Splits | No | The paper references various datasets but does not provide specific details on training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only vaguely mentions 'server facilities' in the acknowledgments. |
| Software Dependencies | No | The paper describes algorithms and models (e.g., logistic regression, Levenshtein automaton) but does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | Our method uses three parameters: d, num, and p. We set these to 4, 150, and 1%, unless otherwise stated. We separately study the performance of our method across variations in these parameters. |